Code Generation

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Code generation asks how models can turn requests, examples, images, or formal specifications into programs that work in real settings, from short functions to whole repositories and domain-specific software. Common approaches include prompting and fine-tuning, retrieval of relevant code or documentation, constrained decoding, and agents that iteratively run, test, and repair their outputs. Multimodal systems also use rendered interfaces, charts, and diagrams as input or feedback, while reinforcement learning can use execution tests, compilers, or formal verifiers as training signals. Benchmarks such as HumanEval, MBPP, LiveCodeBench, and repository-level tasks show progress, but passing tests or compiling does not guarantee that code meets its intended behavior, visual requirements, security needs, or deployment constraints.

Latest papers

313 papers42 papers in the last four weeks, up 68% on the four weeks before. 0.2% of all new papers.

COBALT: Contextual Bandit Learning with Offline Trajectories for Multi-Turn Code Generation
Feb 3, 2026Ziru Chen, Dongdong Chen, Ruinan Jin +3Code Generation RL for Code Generation
The Ohio State University. · Microsoft.
From Charts to Code: A Hierarchical Benchmark for Multimodal Models
Oct 20, 2025Jiahao Tang, Henry Hengyuan Zhao, Lijian Wu +8Data Visualization Chart-to-Code Generation
CSU-JPG, Central South University · National University of Singapore · Nanyang Technological University
TENET: One Step Toward Test-Driven Development for Repository-Level Code Generation
Sep 29, 2025Yiran Hu, Shanchao Liang, Nan Jiang +2Code Generation Agentic Code Generation
Purdue University
Benchmarking Web API Integration Code Generation
Sep 24, 2025Daniel Maninger, Leon Chemnitz, Amir Molzam Sharifloo +2Code Generation Code Generation Evaluation
Technische Universität Darmstadt, Germany · Hessian Center for Artificial Intelligence (hessian.AI), Germany · Pariton AI, Germany +1
EfficientUICoder: A Bidirectional Token Compression Framework for Efficient MLLM-Based UI Code Generation
Sep 15, 2025Jingyu Xiao, Zhongyi Zhang, Yuxuan Wan +3Efficient Multimodal Inference Code Generation
The Chinese University of Hong Kong, Hong Kong, China · Huazhong University of Science and Technology, Wuhan, China · Singapore Management University, Singapore, Singapore +1
Agnostics: Learning to Code in Any Programming Language via Reinforcement with a Universal Learning Environment
Aug 6, 2025Aleksander Boruch-Gruszecki, Yangtian Zi, Zixuan Wu +4RL for Code Generation Code Generation
1Northeastern University · University of Texas · 3Wellesley College
Experimental Analysis of Productive Interaction Strategy with ChatGPT: User Study on Function and Project-level Code Generation Tasks
Aug 6, 2025Sangwon Hyun, Hyunjun Kim, Jinhyuk Jang +2LLM Prompting Code Generation
Adelaide University, Adelaide, SA, Australia · Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
IFEvalCode: Controlled Code Generation
Jul 30, 2025Jian Yang, Wei Zhang, Shukai Liu +9Code Generation Code Language Models
Beihang University · M-A-P · OPPO
FullFront: Benchmarking MLLMs Across the Full Front-End Engineering Workflow
May 23, 2025Haoyu Sun, Huichen Will Wang, Jiawei Gu +2Software Engineering Web Application Generation
Tongji University · University of Washington · Sun Yat-sen University +2
Towards LLM Agents for Earth Observation
Apr 16, 2025Chia Hsiang Kao, Wenting Zhao, Cheryl Lam +10Code Generation LLM Agent Evaluation
Cornell University · Columbia University
BigO(Bench): Can LLMs Generate Code with Controlled Time and Space Complexity?
Mar 19, 2025Pierre Chambon, Baptiste Roziere, Benoit Sagot +1Benchmark Design Code Generation
FAIR at Meta · Inria · Work done at Meta, now working at Mistral AI
Leveraging Metamemory Agent for Enhanced Data-Free Code Generation in Large Language Models
Jan 14, 2025Shengsheng Zhou, Shuai Wang, Liang Ding +5Code Generation Agentic Code Generation
College of Computing and Data Science, Nanyang Technological University, Singapore · School of Computer Science, National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, China · The University of Sydney, Sydney, Australia +3
Evaluation of OpenAI o1: Opportunities and Challenges of AGI
Sep 27, 2024Tianyang Zhong, Zhengliang Liu, Yi Pan +74LLM Evaluation Code Generation

Feb 3, 2026J/K move · Enter open · S save

COBALT: Contextual Bandit Learning with Offline Trajectories for Multi-Turn Code Generation

Ziru Chen, Dongdong Chen, Ruinan Jin, Yingbin Liang+2

The Ohio State University. · Microsoft.

Code Generation RL for Code Generation Contextual Bandits Offline RL

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Training large language models (LLMs) for multi-turn code generation typically relies on online reinforcement learning (RL), in which models learn from repeatedly generated online rollouts. Although effective, online RL can be costly and unstable. In this paper, we present contextual bandit learning with offline trajectories (COBALT), a simple method that reuses offline multi-turn trajectories to efficiently train LLMs for multi-turn code generation. Building on the observation that multi-turn code generation is a one-step recoverable Markov decision process, COBALT first collects code generation trajectories using a reference LLM and divides them into partial trajectories. Then, during online bandit learning, the LLM is trained to complete each partial trajectory through single-step code generation. On LiveCodeBench, COBALT substantially improves R1-Distill 8B and Qwen3 8B by up to 9.0 and 6.2 absolute Pass@1 points and matches the performance of two multi-turn online RL baselines using GRPO and VeRPO. We also perform a perturbation-based analysis of in-context reward hacking and show that Cobalt can improve robustness to misleading execution feedback. Overall, our results demonstrate that Cobalt is a promising solution for multi-turn code generation. Our code and data are available at https://github.com/OSU-NLP-Group/cobalt.